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Record W2212609454

Common Sense and the Charter

2009· article· en· W2212609454 on OpenAlexaffabout
David Schneiderman

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSupreme courtCommon senseCommon lawCharterProportionality (law)LawPolitical scienceEstateLegitimacyLaw and economicsSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

How common are resorts to ‘common sense’ in Charter decision making at the Supreme Court of Canada? It is argued that the Court’s capacity to name reality for everyone is a powerful one and so becomes a site of competition among rival conceptions of common sense. This competition over naming common sense was in operation in the Supreme Court rulings of Gosselin and Chaoulli. In the first case, the Court declared that discriminating against young people in the provision of welfare at less than subsistence levels was supported by common sense. In the second case, a dispute concerning a provincial ban on private health insurance, members of the Court acknowledged the possibility that there could be more than one version of what constitutes common sense. This had the perverse effect, however, of equating empirical evidence offered by Quebec with conjecture offered by the claimants to overturn the ban. The Court also has had recourse to common sense in the course of its proportionality inquiry under section 1. Alive to the legitimacy problems associated with resorting to common sense in order to invalidate legislation, the Court, in these cases, has preferred to rest its proportionality analysis on the seemingly more rigorous and democracy-promoting least restrictive means requirement. Common sense, in this way, serves less controversial purposes but nevertheless underscores the power of the Court to declare what is conventional wisdom for all.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.265
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2009
Admission routes2
Has abstractyes

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